Top-k贴近度中心的快速简便计算
Fast and Simple Computation of Top-k Closeness Centralities
摘要：
贴近度是一个重要的中心性度量，广泛应用于现实世界的复杂网络分析。特别是，在过去的十年中，选择k个最中心节点的问题已经被深入分析。然而，即使对于不是很大的网络，这个问题在实际中也是计算上难以处理的：事实上，Abboud等人最近已经证明了它的复杂性与全对最短路径（简称APSP）问题的复杂性严格相关，对于APSP问题，没有亚立方“组合”算法。在本文中，我们提出了一个新的算法来选择的k个最接近的中心节点的图。在实践中，该算法显着改善了APSP方法，即使其最坏情况下的时间复杂度是相同的。例如，该算法能够在几十秒内计算出前k个节点，即使应用于具有数百万个节点和边的真实网络。我们还将通过实验证明，我们的算法大大优于最近设计的算法，奥尔森等人提出的。最后，我们将新算法应用于IMDB协作网络中最核心的演员的计算，其中两个演员是联系在一起，如果他们在一部电影中一起玩。
英文摘要：
Closeness is an important centrality measure widely used in the analysis of real-world complex networks. In particular, the problem of selecting the k most central nodes with respect to this measure has been deeply analyzed in the last decade. However, even for not very large networks, this problem is computationally intractable in practice: indeed, Abboud et al have recently shown that its complexity is strictly related to the complexity of the All-Pairs Shortest Path (in short, APSP) problem, for which no subcubic "combinatorial" algorithm is known. In this paper, we propose a new algorithm for selecting the k most closeness central nodes in a graph. In practice, this algorithm significantly improves over the APSP approach, even though its worst-case time complexity is the same. For example, the algorithm is able to compute the top k nodes in few dozens of seconds even when applied to real-world networks with millions of nodes and edges. We will also experimentally prove that our algorithm drastically outperforms the most recently designed algorithm, proposed by Olsen et al. Finally, we apply the new algorithm to the computation of the most central actors in the IMDB collaboration network, where two actors are linked if they played together in a movie.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
